A novel sequential three-way decision model with autonomous error correction

As an approach to granular computing, the sequential three-way decision (S3WD) model has been widely studied in practical applications. In terms of improving the accuracy of the S3WD model, existing studies have achieved fruitful results. However, the two types of classification errors and two types of uncertain classifications caused by a probabilistic rough set model have received less consideration, which will result in a higher error classification rate (ECR) in the decision process. In this paper, from the perspective of the subdivision of granules, a new sequential three-way decision model with autonomous error correction (S3WD-AEC) is proposed to reduce the ECR. First, two types of errors correction and two types of effective classifications in the S3WD model are defined. Next, according to the process of information granulation, four subdivisions of equivalence classes are discussed in detail. Subsequently, the total ECR composed of the positive and negative regions in each granularity layer is proved to gradually decrease with the subdivision of the equivalence classes. Then, during the S3WD process, four commonly used clustering algorithms are introduced to select a portion of the equivalence classes near the boundary region for further subdivision, implementing an error correction for some misclassified objects. Finally, the experimental results show that the S3WD-AEC model has a smaller ECR compared with the S3WD model. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a new sequential three-way decision model with autonomous error correction to reduce error classification rate by subdividing granules. Experimental results show that the proposed model outperforms traditional models in terms of accuracy.